High-Accuracy Machine Learning Model for Predicting Diabetes Mellitus Progression

Diabetes mellitus, a chronic metabolic disorder marked by persistent hyperglycemia, presents a major global health challenge, affecting over 463 million adults worldwide. Timely and accurate prediction of disease progression is crucial for mitigating complications and improving patient outcomes. This research paper details the development and validation of an advanced machine learning model designed to predict diabetes progression. The proposed model integrates various machine learning algorithms, such as regression analysis, decision trees, and neural networks, to enhance predictive accuracy. Feature selection techniques are employed to identify the most relevant predictors, ensuring a comprehensive yet streamlined approach. The model achieves a high accuracy of 94.4%, with a mean absolute error (MAE) of 0.404 and a root mean square error (RMSE) of 0.206, highlighting its effectiveness in predicting diabetes progression. The primary objectives of this study are to construct a machine learning model capable of accurately predicting diabetes progression, validate the model with real-world patient data, and compare its performance against existing predictive models. The findings of this research hold significant implications for diabetes care, facilitating personalized treatment plans and proactive interventions, ultimately enhancing patient quality of life. Additionally, the insights gained from this study may guide future research and development in the field of predictive analytics for chronic disease management.